CN111344804A - System and method for displaying electronic health records - Google Patents

Abstract

A system (300) configured to analyze electronic medical records comprising: a user interface (310) configured to receive input from a user and to receive a request for patient information; and a processor (320) comprising: a patient cohort generator (350) configured to: (i) tracking user input; (ii) identifying patient information accessed through the user interface and patient parameters associated with a patient; (iii) associating a patient into a patient cohort based on the patient parameter; (iv) identifying, for the patient queue, a type of information most frequently accessed by the user; and (v) associating the identified type of information with the patient cohort; and a record identifier (370) configured to: (i) associating the patient for whom patient information is requested with a patient cohort; and (ii) identify a type of information associated with the cohort based on the patient cohort associated with the patient.

Description

System and method for displaying electronic health records

Technical Field

The present disclosure relates generally to methods and systems for displaying electronic health records.

Background

The electronic medical records display interface has limited display space. However, patients typically have many associated medical records. Displaying all of the content of a medical record associated with a patient can result in information overload, and the display space dedicated to portions of the medical record limits the amount of relevant information that a clinician or other user can consume quickly and easily without requiring extensive manipulation of the data. This manipulation of information is very time consuming.

One solution to this information overload problem is to display on the screen portions of the medical record, such as the first few sentences of the last few documents or excerpted segments of these documents. However, this can inadvertently create blind spots where important content of these documents related to popular clinical scenarios may not appear in the displayed segment, or meaningful information in the history may be hidden while less meaningful information from the most recent records is displayed.

Disclosure of Invention

There is a continuing need to improve the display of relevant portions of electronic health records.

The present disclosure relates to inventive methods and systems for identifying, analyzing, and displaying electronic health records. Various embodiments and implementations herein relate to a medical records display system that generates a patient cohort with associated commonly used medical records. The system tracks the user of the interface and identifies which records are typically reviewed for which patients. Patients with similar parameters are aggregated into a patient cohort, and common query records for the cohort are identified. When the system receives a query for information about a new patient, the most closely related patient cohort is identified, and then relevant patient medical records are identified based on the record type associated with the identified patient cohort. The system may then display the identified relevant patient medical records.

In general, in one aspect, a system for analyzing electronic medical records is provided. The system comprises: a user interface configured to receive input from a user while viewing one or more electronic medical records, and further configured to receive a request for patient information. The system further includes a processor, the processor comprising: a patient cohort generator configured to: (i) tracking user input; (ii) identifying patient information accessed through the user interface based on the user input and also identifying one or more patient parameters associated with the patient; (iii) associating two or more patients into a patient cohort based on the one or more patient parameters; (iv) identifying, for the patient cohort, one or more types of information most frequently accessed by the user; and (v) associating one or more types of identified information with the patient cohort; and a record identifier configured to: (i) associating the patient for whom patient information is requested with a patient cohort; and (ii) identify the one or more types of information associated with the cohort based on the patient cohort associated with the patient.

According to an embodiment, the system further comprises a patient cohort database configured to store information about one or more generated patient cohorts.

According to an embodiment, the patient queue generator is further configured to identify a particular user of the user interface among a plurality of users, and further configured to track user input for the identified particular user. According to an embodiment, the identification of the particular user of the user interface is based at least in part on one or more favorable outcomes for one or more patients.

According to an embodiment, the patient cohort generator is further configured to refine the one or more types of associated identified information using data from additional medical information sources.

According to an embodiment, the patient cohort identified by the record identifier is based at least in part on additionally obtained information about the patient.

According to an embodiment, the user interface is further configured to display one or more identified types of information associated with the identified patient cohort. According to an embodiment, the user interface is further configured to display a portion of the identified type of information associated with the identified patient cohort.

According to another aspect, a method for analyzing an electronic medical record is provided. The method comprises the following steps: generating a patient cohort comprising the steps of: (i) tracking activity of one or more users of the electronic medical records interface; (ii) identifying patient information accessed by the one or more users through the electronic medical records interface based on the analysis of the tracked activities and also identifying one or more patient parameters associated with a patient; (iii) associating two or more patients into a patient cohort based on the one or more patient parameters; (iv) identifying, for the patient cohort, one or more types of information most frequently accessed by the user; and (v) associating one or more types of identified information with the patient cohort. The method further comprises the steps of: receiving a request from a user for information about a patient; associating the patient with a patient cohort based on one or more parameters of the patient; and identifying the one or more types of information associated with the cohort based on the associated patient cohort.

According to an embodiment, the step of generating a patient cohort further comprises: identifying a particular user of the electronic medical records interface for tracking.

According to an embodiment, the identification of the particular user of the user interface is based at least in part on one or more favorable outcomes for one or more patients.

According to an embodiment, the method further comprises the steps of: refining one or more types of identified information associated with the patient cohort using additional medical information.

According to an embodiment, the method further comprises the steps of: additionally obtained medical information about the patient is analyzed.

According to an embodiment, the method further comprises the steps of: displaying the one or more types of information associated with the identified queue on a user interface. Displaying the one or more types of information associated with the identified queue includes: only the identified portion of the one or more types of information is displayed.

In various embodiments, a processor or controller may be associated with one or more storage media (generally referred to herein as "memory," e.g., volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM, floppy disks, compact disks, optical disks, magnetic tape, etc.). In some embodiments, the storage medium may be encoded with one or more programs that, when executed on one or more processors and/or controllers, perform at least some of the functions discussed herein. Various storage media may be fixed within a processor or controller or may be transportable, such that one or more programs that may be stored thereon are loaded into a processor or controller to implement various aspects discussed herein. The terms "program" or "computer program" are used herein in a generic sense to refer to any type of computer code (e.g., software or microcode) that can be employed to program one or more processors or controllers.

As used herein, the term "network" refers to any interconnection of two or more devices (including controllers or processors) that facilitates the transfer of information (e.g., for device control, data storage, data exchange, etc.) between any two or more devices and/or between multiple devices coupled to the network. As should be readily appreciated, various embodiments of networks suitable for interconnecting multiple devices may include any of a variety of network topologies and employ any of a variety of communication protocols. Additionally, in various networks according to the present disclosure, any one connection between two devices may represent a dedicated connection between the two systems, or alternatively, a non-dedicated connection. In addition to carrying information intended for both devices, such a non-dedicated connection may carry information that is not necessarily intended for either of the two devices (e.g., an open network connection). Further, it should be readily appreciated that the various networks of devices as discussed herein may employ one or more wireless, wired/cable, and/or fiber optic links to facilitate the transfer of information throughout the network.

It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein. It should also be appreciated that terms explicitly employed herein may also appear in any disclosure incorporated by reference, and such terms should be given the most consistent meaning to the particular concepts disclosed herein.

Various embodiments are directed to a method and system for intelligently selecting segments to be displayed on an EMR interface. The method begins by classifying certain users as "expert users" by identifying those users that exhibit a pattern of easy interface navigation (e.g., a low number of interactions or a short time to access information they consider relevant to the patient). When the experts use the system, the method tracks the type of information accessed (as entered by applying natural language processing to the accessed information and relating to the clinical ontology) and records the information along with demographics, vital signs, diagnoses, and the like to categorize the information about the associated patient. Thus, the most important types of information can be listed and ranked across different patient queues.

Thereafter, when any user retrieves a record for a particular patient, the method identifies the patient's queue and retrieves a list of ranked information types. The method then performs NLP across the patient's EMRs to identify all entries that match the ontological concepts on the ranked list. Thereafter, the snippet from the highest ranked entry may be displayed on the main screen of the EMR for that patient. Upon clicking on the segment, the user is presented with the complete entry from which the segment was obtained.

These and other aspects will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.

Drawings

In the drawings, like reference numerals generally refer to the same parts throughout the different views. Moreover, the drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles disclosed herein.

Fig. 1 is a flow diagram of a method for displaying an electronic health record, according to an embodiment.

Fig. 2 is a flow diagram of a method for displaying an electronic health record, according to an embodiment.

Fig. 3 is a schematic representation of a system for displaying an electronic health record, according to an embodiment.

Detailed Description

The present disclosure describes various embodiments of a system for identifying and displaying electronic health records. More generally, applicants have recognized and appreciated that it would be beneficial to provide a system that more efficiently utilizes the limited display of a medical records user interface. The system tracks the user of the interface and identifies which records are typically reviewed for which patients. Patients with similar parameters are aggregated into a patient cohort, and common reference records for the cohort are identified. When the system receives a query for information about a new patient, the most closely related patient cohort is identified, and then relevant patient medical records are identified based on the record type associated with the identified patient cohort. According to an embodiment, the system may display the identified relevant patient medical records on a user interface for review by a clinician, patient, or other user. Since only portions of the medical records can be displayed on the user interface at any given time, the system can utilize information from the generated patient cohort and associated records to identify which portion or portions of the records should be preferentially displayed.

Referring to FIG. 1, in one embodiment, a flow diagram of amethod 100 for identifying an electronic health record is shown. At step 110 of the method, a medical records display system is provided. The medical records display system may be any system described herein or otherwise contemplated.

At step 120 of the method, the medical records system generates a patient cohort. As described below, generating a patient queue includes one or more of steps 130-138. The patient cohort will include a plurality of patients that are related based on one or more parameters. For example, patients may be correlated based on clinical context such as disease, symptoms, treatment, medical history, and/or other clinical context. Patients may be correlated based on patient demographics such as gender, age, background, and/or other patient demographics. Patients may be correlated based on which record or records a user for a patient most often accesses or views. The patient may be identified as relevant based on a combination of several of these and/or other parameters.

The generated patient queue will also include an identification of one or more types of information (e.g., medical records) that are most frequently accessed, viewed, or otherwise utilized by a user of the medical records system with respect to the patients in the patient queue. Thus, if a user of the system frequently visits a patient's X-rays in an orthopaedic clinical setting, a patient queue including orthopaedic patients may have X-rays as one of the types of information associated with the queue. Thus, if multiple patient cohorts are generated, each cohort may be associated with a unique and/or overlapping record type or information type.

Atstep 130 of the method, the medical record system tracks the activities of one or more users of the system. For example, the user may be a clinician or other expert viewing patient records via a user interface of the system. According to an embodiment, a user interface instrumentation tool is utilized to monitor clinician-user interaction with the system. The monitoring aspect of the modules of the system identifies what patient notes to view, which records or record types to access, and/or other information.

According to an embodiment, at step 131 of the method, the system or the user identifies one or more specific users of the system for tracking. These identified particular users will be utilized in preference to or in lieu of other users to identify patient cohorts and/or record types associated with patient cohorts. For example, a user presenting a simple and straightforward navigation or retrieval pattern or history may be considered an expert user because they will go immediately to or otherwise retrieve relevant textual information. Various metrics may be used in this manner to identify expert users, such as the time spent using the interface per session, the average time between interface clicks, the number of times the user clicks "back", the complexity of the "tree" constructed by the user navigation (e.g., whether the user has encountered many "mustaches" before finding the desired information, or whether the user has accessed the desired information directly). In an alternative embodiment, a clinician determined to make a decision leading to a positive result may be identified as a particular user of the system for tracking, which may help refine the expert group. In some embodiments, expert users may be considered experts for all patients for the purpose of identifying the most relevant information, or expert titles may be granted on a per-queue basis. For example, clinician a may be designated as an expert in cohort a (e.g., >40 year old cardiac patients) and thus may be used to determine the most relevant information, but is not an expert in cohort B (e.g., pediatric hematology patients) and thus may use other experts to identify relevant information for that cohort. One or more specific users may also be identified by a programmer or user of the medical recording system. For example, a programmer or user may wish to have a senior clinician or experienced user as the particular user identified for analysis and tracking.

According to an embodiment, the system tracks specific information viewed or accessed by a user. This specific information may be in addition to or as an alternative to tracking the type of record accessed by the user. The system may then identify specific information within the record or record type that is typically accessed by the user. This can be utilized downstream to help identify the record type and the information within the record type to provide to the clinician. According to an embodiment, the system may analyze specific information accessed by the user to identify similar or related specific information in other records or other record types in order to provide the clinician with the most relevant information.

According to an embodiment, the medical records system utilizes eye tracking software or algorithms to track or identify information most often viewed or accessed by a user of the medical records system. For example, the user interface or system may include or otherwise communicate with a camera (e.g., a camera of a wearable device) that identifies and tracks objects, records, or areas of the user interface that are most frequently and/or most intensely viewed by the user. These objects, records or areas may be identified as the most accessed or important objects, records or areas.

According to an embodiment, a medical records system utilizes Natural Language Processing (NLP) to identify and/or extract information from one or more records identified by a user via tracking. For example, a user may utilize a user interface to access and view unstructured reports or data, such as handwritten notes. Medical record systems identify records accessed or viewed by a user via tracking (e.g., eye tracking) and extract information from the records using NLP or other data extraction or analysis methods.

At step 132 of the method, the system identifies patient information accessed by one or more users through an electronic medical records interface based on analysis of the tracked activity. For example, the system may record the information or record the sources accessed by one or more users and store the information in a database. The system may then retrieve the stored information for immediate or downstream analysis, as described herein or otherwise contemplated.

Also at step 132 of the method, the system identifies one or more patient parameters associated with the patient in order to create a patient cohort of related patients. For example, patients may be correlated based on clinical context such as disease, symptoms, treatment, medical history, and/or other clinical context. Patients may be correlated based on patient demographics such as gender, age, background, and/or other patient demographics. Patients may be correlated based on which record or records a user for a patient most often accesses or views. The patient may be identified as a relevant patient based on a combination of several of these and/or other parameters.

At step 134 of the method, the system associates two or more patients into a patient cohort based on one or more patient parameters. Patients with similar parameters may be associated into the same patient cohort. Similarity may be based on a threshold, a number of similar or dissimilar parameters, the severity or range of one or more parameters, input from a programmer or user of the system, demographics, record type, disease, and/or many other patient factors. The patient cohort may be generated or stored in a memory or database, or may be otherwise identified or generated.

At step 136 of the method, the system identifies one or more types of information most frequently accessed by the user for a particular patient cohort. For example, the system may record information or record sources accessed by one or more users and identify which source of the record is most frequently used. This may be based on thresholds, rankings, and/or machine learning mechanisms. Different patient queues may have the same commonly accessed record type, some overlapping commonly accessed record types, or non-overlapping commonly accessed record types. In some embodiments, the device may utilize the expert user-only access history (across all queues or for that particular queue) to identify which types of information the expert user has most frequently accessed for the patients of that queue. The method may apply natural language processing at this step to extract concepts identified by the clinical ontology from documents accessed by experts for patients in the cohort, and then rank the concepts by frequency of access.

Atstep 138 of the method, the system associates one or more types of identified information or records with a patient cohort. The identified record type of frequent visits may be associated with a patient cohort in a memory or database, or may be otherwise identified or associated with a patient cohort. Thus, when a clinician or user accesses a patient in the patient queue, the patient and/or patient queue will be associated with one or more types of identified information or records.

Atstep 139 of the method, the system uses the additional information to modify the patient cohort and/or one or more types of identified information or records associated with the patient cohort. For example, one or more patients and/or one or more identified records in the cohort may be ranked, filtered, added, removed, or otherwise modified using a clinical database or other relevant information source. For example, clinical concepts associated with the cohort for diagnostic or therapeutic decision-making may be identified using additional information and thus may be preferentially reported. Among many additional sources of information, there are databases such as Medscape, PubMed, wikipedia, medical journals, other knowledge-based databases, clinician-selected data, and more.

Multiple patient queues may be generated one or more times using a large corpus or database of patients, patient records, and user tracking information. The generated plurality of patient cohorts may be stable, may be continuously or periodically updated, and/or may be reformulated as needed. Once the patient cohort is created, the medical records system utilizes the multiple patient cohorts to optimize the information provided to the clinician for future patient use. Thus, at step 140 of the method, the medical record system receives a request for information about the patient. The request may come from a clinician or any other user of the medical records system, including the patient. The information about the patient may be medical history, patient parameters, and/or medical records, among many other types of information.

At step 150 of the method, the system associates the patient with one of the generated plurality of patient cohorts based at least in part on one or more patient parameters. The patient may be associated with a patient cohort that is most similar to the patient. Similarity may be based on a threshold, a number of similar or dissimilar parameters, the severity or range of one or more parameters, input from a programmer or user of the system, demographics, record type, disease, and/or many other patient factors. Patient associations with patient cohorts may be generated or stored in memory or databases, or may be otherwise identified.

At step 142 of the method, the system analyzes additional information about the patient in order to identify an appropriate patient cohort and/or to reduce redundancy in the system. For example, once a new patient is identified in the patient queue, the patient's notes, social media, activity patterns, and/or other data sources may be analyzed, for example, using semantic or natural language processing. This information may modify or further refine the patient cohort to which the patient belongs, or may modify or further refine which records associated with the identified patient cohort are provided or preferred.

At step 160 of the method, the system identifies one or more records of the patient that include information that matches the type of the one or more identified information associated with the patient cohort based on the patient cohort associated with the patient. For example, starting with the ranked list of ontological concepts described above with respect to step 136, any ontological concepts are extracted from the patient's records using NLP and then compared to the ranked list of concepts for the cohort to determine which documents include concepts that match the list. The system may then select the number of documents to be displayed (e.g., a preconfigured number or a number that may be displayed on the UI depending on the current display configuration and the size of the segment to be displayed, as explained below). For example, the system may select the document that matches the highest ranked concept in the ranked list. In some embodiments, to avoid accumulating information, the system may select only one document for each concept in the ranked list. In such an embodiment, for example, even if three documents include concept #1 in the ranked list, the system may select only one (e.g., randomly, based on the latest additional concepts included in the documents, or based on other selection criteria) and continue to select the document containing concept # 2. This therefore optimizes the information provided to the clinician based on the patient associated with the appropriate patient cohort.

According to an embodiment, the system identifies, highlights, or otherwise provides a particular portion or segment of the identified record. These identified portions or segments may be based on the identified record type, information about the patient, identification of preferred segments or portions based on the user analysis described above, or using any other method.

Atstep 170 of the method, the identified one or more records are displayed in some form. For example, the interface may display one or more of an identification of the document (e.g., "radiology report on 1/2017"), a link to the identified document, a fragment of text or image data from the document, or the entire document. Any method or system may be used to present the identified information. For example, information may be presented to a user in real-time, e.g., via a user interface of a mobile device, laptop, desktop, wearable device, or any other computing device. The results may be presented by any user interface that allows information to be presented (e.g., a microphone or text input), as well as many other types of user interfaces. Alternatively, the results may be presented to a computing device or automated system. In some embodiments, an area of the patient dashboard may be designated to display the results of the method. The dashboard may be displayed in response to selection or other identification of the patient from another screen of the user interface (e.g., a patient search or ward overview), including other information about the patient, such as demographic information, vital signs, assigned staff, clinical decision support algorithm output, and so forth.

According to an embodiment, the system preferentially displays the identified portions or segments of the identified records. These identified sections or segments may be based on the identified record type, information about the patient, identification of preferred segments or sections based on the user analysis described above, or using any other method. In some embodiments, the system may select a segment of text (or image or other data) near the location where the ontology concepts matching the ranked list are extracted. According to an embodiment, these portions or segments are displayed with links to provide evidence or additional information about one or more clinical issues in the patient record. According to an embodiment, the portions or segments are displayed with a link to a clinical database indicating the clinical value of one or more clinical questions for patient treatment.

Referring to fig. 3, in one embodiment, the figure is a schematic representation of amedical record system 300.System 300 may include any of the modules, elements, databases, processors, and/or other components described herein or otherwise contemplated.

According to an embodiment, thesystem 300 includes auser interface 310 to receive queries from users, track user interactions with the system, and/or provide identified information to users. The user interface may be any device or system that allows for the communication and/or reception of information, such as a speaker or a screen, among many other types of user interfaces. The information may also be transmitted to and/or received from a computing device or an automation system. The user interface may be located with one or more other components of the system, or may be remote from the system and communicate via a wired and/or wireless communication network.

According to an embodiment, thesystem 300 includes a processor 320 that performs one or more steps of the method, and may include one or more modules. Processor 320 may be formed of one or more modules and may include, for example, memory 330. Processor 320 may take any suitable form including, but not limited to, a microcontroller, a plurality of microcontrollers, a circuit, a single processor, or a plurality of processors. Memory 330 may take any suitable form, including non-volatile memory and/or RAM. The nonvolatile memory may include a Read Only Memory (ROM), a Hard Disk Drive (HDD), or a Solid State Drive (SSD). The memory may store an operating system, etc. RAM is used by processors to temporarily store data. According to an embodiment, the operating system may contain code that, when executed by a processor, controls the operation of one or more components ofsystem 300.

According to an embodiment, thesystem 300 includes a patient cohort generator 350, which may be a processor, a component of one or more processors, and/or a software algorithm. The patient cohort generator 350 creates one or more patient cohorts as described herein or otherwise contemplated. The patient cohort will include a plurality of patients that are related based on one or more parameters. For example, patients may be correlated based on clinical context such as disease, symptoms, treatment, medical history, and/or other clinical context. Patients may be correlated based on patient demographics such as gender, age, background, and/or other patient demographics. Patients may be correlated based on which record or records a user for a patient most frequently visits or views. The patient may be identified as relevant based on a combination of several of these and/or other parameters. The generated patient queue will also include an identification of one or more types of information (e.g., medical records) that are most frequently accessed, viewed, or otherwise utilized by a user of the medical records system with respect to the patients in the patient queue.

According to an embodiment, the patient cohort generator 350 creates one or more patient cohorts by tracking activities of one or more users of the system, identifying patient information accessed by the one or more users through an electronic medical records interface, identifying one or more patient parameters associated with the patients to create a patient cohort for the relevant patients, and associating two or more patients into the patient cohort based on the one or more patient parameters. The patient queue generator 350 also identifies one or more types of information most frequently accessed by the user for a particular patient queue and associates the identified one or more types of information or records with the patient queue. The patient queue generator 350 may also identify portions of the segments of these records for preferential display. The patient cohort generator 350 may review the additional information to modify one or more types of patient cohorts and/or identified information or records associated with patient cohorts. For example, the patient cohort generator 350 may consult additional sources of medical information 380, such as Medscape, PubMed, wikipedia, medical journals, other knowledge-based databases, clinician-selected data, and more.

According to an embodiment, the patient queue generator 350 creates one or more patient queues using the corpus of medical information 340 (e.g., information about a plurality of patients). The corpus of medical information may be a database of patient information associated with data about records typically accessed for these patients.

According to an embodiment, the patient cohort generator 350 stores the generated patient cohort and associated record type or information in a database 360, which may be a component of the system, or may be stored locally or remotely, and periodically and/or continuously in communication with the system.

According to an embodiment, thesystem 300 includes arecord identifier 370, which may be a processor, a component of one or more processors, and/or a software algorithm. Therecord identifier 370 receives, analyzes, and/or interprets requests for information about a patient received via theuser interface 310. The request may come from a clinician or any other user of the medical records system, including the patient. The information about the patient may be medical history, patient parameters, and/or medical records, among many other types of information.

Therecord identifier 370 associates the patient with one of the generated plurality of patient cohorts based at least in part on one or more patient parameters. A patient may be associated with a patient cohort that is most similar to the patient, where similarity may be based on, for example, a threshold similarity may be based on a threshold, a number of similar or dissimilar parameters, a severity or range of one or more parameters, input from a programmer or user of the system, demographics, record type, disease, and/or a number of other patient factors.

Therecord identifier 370 may analyze additional information about the patient, such as the patient's notes, social media, activity patterns, and/or other data sources, to facilitate identifying an appropriate patient cohort and/or to reduce redundancy in the system.

Therecord identifier 370 identifies one or more types of information or records associated with a patient queue and most commonly accessed or utilized based on the queue associated with the patient. Therecord identifier 370 may also identify, highlight, or otherwise provide a particular portion or segment of the identified record. Therecord identifier 370 may then transmit the patient information including the identified record and/or the identified portion or segment of the record to theuser interface 310, a server or database, or another location.

As defined and used herein, all definitions should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms.

The indefinite articles "a" and "an" as used in the specification and in the claims should be understood to mean "at least one" unless explicitly indicated to the contrary.

The phrase "and/or" as used herein in the specification and in the claims should be understood to mean "either or both" of the elements so connected, i.e., the elements present in combination in some cases and present in isolation in other cases. Multiple elements listed with "and/or" should be interpreted in the same manner, i.e., "one or more" of the elements so connected. In addition to elements explicitly identified by the "and/or" clause, other elements may be present, whether related or unrelated to those elements specifically identified.

As used herein in the specification and in the claims, "or" should be understood to have the same meaning as "and/or" as defined above. For example, when an item in a list is divided, "or" and/or "should be interpreted as inclusive, i.e., including at least one of a plurality of elements or a list of elements, but also including more than one, as well as additional unlisted items. To the contrary, terms such as "only one of" or "exactly one of," or "consisting of," when used in the claims, are intended to mean that it includes a plurality of elements or exactly one of the list of elements. In general, the term "or" when preceded by an exclusive term (e.g., "any," "one of," "only one of," or "exactly one of") as used herein should only be construed to mean an exclusive alternative (i.e., "one or the other but not both").

As used herein in the specification and in the claims, the phrase "at least one," when referring to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each element specifically listed within the list of elements, and not excluding any combinations of elements in the list of elements. This definition also allows that, in addition to elements specifically identified within the list of elements referred to by the phrase "at least one," there may be other elements, whether related or unrelated to those specifically identified elements.

It will also be understood that, in any method claimed herein that includes more than one step or action, the order of the steps or actions of the method is not necessarily limited to the order in which the steps or actions of the method are recited, unless specifically indicated to the contrary.

In the claims, as well as in the specification above, all transitional phrases such as "comprising," "including," "carrying," "having," "containing," "involving," "holding," "including," and the like are to be understood to be open-ended, i.e., to mean including but not limited to. The transitional phrases "consisting of" and "consisting essentially of" shall be closed or semi-closed transitional phrases, respectively.

While several inventive embodiments have been described and illustrated herein, those of ordinary skill in the art will readily envision a variety of other means and/or structures for performing the functions and/or obtaining the results and/or one or more of the advantages described herein, and each of such variations and/or modifications is deemed to be within the scope of the inventive embodiments described herein. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings of the present invention is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, embodiments of the invention may be practiced otherwise than as specifically described and claimed. Inventive embodiments of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims (20)

1. A system (300) configured to analyze electronic medical records, the system comprising:

a user interface (310) configured to receive input from a user while viewing one or more electronic medical records, and further configured to receive a request for patient information; and

a processor (320), comprising:

a patient cohort generator (350) configured to: (i) tracking user input; (ii) identifying patient information accessed through the user interface based on the user input and also identifying one or more patient parameters associated with a patient; (iii) associating two or more patients into a patient cohort based on the one or more patient parameters; (iv) identifying, for the patient cohort, one or more types of information most frequently accessed by the user; and (v) associating one or more types of identified information with the patient cohort; and

a record identifier (370) configured to: (i) associating the patient for whom patient information is requested with a patient cohort; and (ii) identify the one or more types of information associated with the cohort based on the patient cohort associated with the patient.

2. The system as recited in claim 1, further comprising a patient cohort database (360) configured to store information regarding one or more generated patient cohorts.

3. The system of claim 1, wherein the patient cohort generator is further configured to identify a particular user of the user interface among a plurality of users, and further configured to track user input for the identified particular user.

4. The system of claim 3, wherein the identification of the particular user of the user interface is based at least in part on one or more favorable outcomes for one or more patients.

5. The system of claim 1, wherein the patient cohort generator is further configured to refine the one or more types of associated identified information using data from additional sources of medical information (380).

6. The system of claim 1, wherein patient cohort identification by the record identifier is based at least in part on additionally obtained information about the patient.

7. The system of claim 1, wherein the user interface is further configured to display one or more of the identified types of information associated with the identified patient cohort.

8. The system of claim 7, wherein the user interface is further configured to display a portion of the identified type of information associated with the identified patient cohort.

9. The system of claim 1, wherein:

the processor is further configured to determine whether to classify the user as an expert user, and

in identifying one or more types of information most frequently accessed by the user, the processor is configured to identify one or more types of information most frequently accessed by an expert user of the system.

10. The system of claim 9, wherein, in determining whether to classify the user as an expert user, the processor is configured to analyze the user's input to determine a usability metric associated with the user, wherein users displaying a relatively high usability of the system are classified as expert users.

11. A method (100) for analyzing an electronic medical record, the method comprising the steps of:

generating (120) a patient cohort comprising the steps of:

tracking (130) activities of one or more users of the electronic medical records interface;

identifying (131) patient information accessed by the one or more users through the electronic medical records interface based on the analysis of the tracked activities, and further identifying one or more patient parameters associated with the patient;

associating (134) two or more patients into a patient cohort based on the one or more patient parameters;

identifying (136), for the patient cohort, one or more types of information most frequently accessed by the user; and

associating (138) one or more types of identified information with the patient cohort; receiving (140) a request from a user for information about a patient;

associating (150) the patient with a patient cohort based on one or more parameters of the patient; and

identifying (160) the one or more types of information associated with the cohort based on an associated patient cohort.

12. The method as recited in claim 11, wherein the step of generating a patient queue further includes identifying (131) a particular user of the electronic medical records interface for tracking.

13. The method of claim 12, wherein the identification of the particular user of the user interface is based at least in part on one or more favorable outcomes for one or more patients.

14. The method of claim 11, further comprising the steps of: refining (139) one or more types of identified information associated with the patient cohort using additional medical information.

15. The method of claim 11, further comprising the steps of: analyzing (142) additionally obtained medical information about the patient.

16. The method of claim 11, further comprising the steps of: displaying (170) the one or more types of information associated with the identified queue on a user interface.

17. The method of claim 16, wherein displaying the one or more types of information associated with the identified queue comprises displaying only the identified portion of the one or more types of information.

18. The method of claim 11, further comprising:

determining whether to classify the user as an expert user,

wherein identifying one or more types of information most frequently accessed by the user comprises identifying one or more types of information most frequently accessed by an expert user of the system.

19. The system of claim 18, wherein determining whether to classify the user as an expert user comprises: analyzing the user's input to determine a usability metric associated with the user, wherein users showing a relatively high usability of the system are classified as expert users.

20. A non-transitory machine-readable storage medium encoded with instructions for displaying a portion of a medical record, the non-transitory machine-readable storage medium comprising:

instructions for presenting a user interface to a plurality of users to view one or more electronic medical records associated with a plurality of patients;

instructions for tracking usage of the user interface by the plurality of users;

instructions for classifying at least one user of a plurality of users as an expert user based on tracked usage for the expert user;

identifying a type of information commonly accessed by the expert user for a first cohort of patients;

receiving, via the user interface, a request for data related to a patient;

determining that the patient belongs to the first cohort;

locating at least one record stored for the patient, the at least one record including information of the type of information commonly accessed by the expert user for a first cohort of patients;

in response to the request for data related to the patient, displaying at least one of a portion of a document, an identifier of the document, or a link to the document.

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